When building portable sensors we often want to calibrate and double-check their readings before allowing them to log data remotely. Whilst developing these we can easily SSH into them and write any results to screen. However what happens when we are in a very remote part of the world, with no laptop, wifi or signal?
Within this tutorial, we look at exploiting the Bluetooth capabilities of a Raspberry Pi Zero (without WiFi) to transmit the initial set of results to a handheld device of our choosing. In our case, it will be through the use of a mobile phone or android tablet, such that we can compare the sensor and GPS readings.
Before we start there are a couple of changes required for the Bluetooth to work. These are outlined below.
We begin by changing the configuration of the installed Bluetooth library:
sudo nano /etc/systemd/system/dbus-org.bluez.service
Here we locate the line starting
ExecStart , and replace it with the following:
ExecStart=/usr/lib/bluetooth/bluetoothd --compat --noplugin=sap ExecStartPost=/usr/bin/sdptool add SP
Having added the ‘compatibility’ flag, we now have to restart the Bluetooth service on the Pi:
sudo systemctl daemon-reload; sudo systemctl restart bluetooth.service;
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Tools and Images to Build a Raspberry Pi n8n server
The purpose of this project is to create a Raspberry Pi image preconfigured with n8n so that it runs out of the box.
n8n is a no-code/low code environment used to connect and automate different systems and services. It is programmed using a series of connected nodes that receive, transform, and then transmit date from and to other nodes. Each node represents a service or system allowing these different entities to interact. All of this is done using a WebUI.
Whevever a new technology is released, two common barriers often prevent potential users from trying out the technology:
The n8n-pi project eliminates these two roadblocks by preconfiguring a working system that runs on easily available, low cost hardware. For as little as $40 and a few minutes, they can have a full n8n system up and running.
This project would not be possible if it was not for the help of the following:
All documentation for this project can be found at http://n8n-pi.tephlon.xyz.
#pi #raspberry pi #raspberry #raspberry-pi
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data